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This isn’t entirely news to people in the field doing research, but it’s important information to keep in mind when anyone starts pushing fMRI (or SPECT) scans into popular media discussions about neurology or psychiatry.

There have been some high profile influencer doctors pushing brain imaging scans as diagnostic tools for years. Dr. Amen is one of the worst offenders with his clinics that charge thousands of dollars for SPECT scans (not the same as the fMRI in this paper but with similar interpretation issues) on patients. Insurance won’t cover them because there’s no scientific basis for using them in diagnosing or treating ADHD or chronic pain, but his clinics will push them on patients. Seeing an image of their brain with some colors overlayed and having someone confidently read it like tea leaves is highly convincing to people who want answers. Dr. Amen has made the rounds on Dr. Phil and other outlets, as well as amassing millions of followers on social media.

>which are known to produce predictable fMRI signal changes in distributed brain regions.

Wondering how they created that baseline. Was it with fMRI data (which has deviance from actual data, as pointed out)? Or was it through other means?

My previous job was at a startup doing BMI, for research. For the first time I had the chance to work with expensive neural signal measurement tools (mainly EEG for us, but some teams used fMRI). and quickly did I learn how absolute horrible the signal to noise ratio (SNR) was in this field.

And how it was almost impossible to reproduce many published and well cited result. It was both exciting and jarring to talk with the neuroscientist, because they ofc knew about this and knew how to read the papers but the one doing more funding/business side ofc didn't really spend much time putting emphasis on that.

One of the team presented a accepted paper that basically used Deep Learning (Attention) to predict images that a person was thinking of, from the fMRI signals. When I asked "but DL is proven to be able to find pattern even in random noise, so how can you be sure this is not just overfitting to artefact?" and there wasn't really any answer to that (or rather the publication didn't take that in to account, although that can be experimentally determined). Still, a month later I saw tech explore or some tech news writing an article about it, something like "AI can now read your brain" and the 1984 implications yada yada.

So this is indeed something probably most practitioners, masters and PhD, realize relatively early.

So now that someone says "you know mindfulness is proven to change your brainwaves?" I always add my story "yes, but the study was done with EEG, so I don't trust the scientific backing of it" (but anecdotally, it helps me)

This study is validating a commonplace fMRI measure (change in blood-oxygenation-level-dependent or BOLD signal) by comparing it with a different MRI technique, one that uses a multiparametric quantitative BOLD model, a different model for BOLD derived from two separate MRI scans which measure two different kinds of signal (transverse relaxation rates), and then multiply/divide by a bunch of constants to get at a value.

I'm a software engineer in this field, and this is my layman-learns-a-bit-of-shop-talk understanding of it. Both of these techniques involve multiple layers of statistical assumptions, and multiple steps of "analysing" data, which in itself involves implicit assumptions, rules of thumb and other steps that have never sat well with me. A very basic example of this kind of multi-step data massaging is "does this signal look a bit rough? No worries, let's Gaussian-filter it".

A lot of my skepticism is due to ignorance, no doubt, and I'd probably be braver in making general claims from the image I get in the end if I was more educated in the actual biophysics of it. But my main point is that it is not at all obvious that you can simply claim "signal B shows that signal A doesn't correspond to actual brain activity", when it is quite arguable whether signal B really does measure the ground truth, or whether it is simply prone to different modelling errors.

In the paper itself, the authors say that it is limited by methodology, but because they don't have the device to get an independent measure of brain activation, they use quantitative MRI. They also say it's because of radiation exposure and blah blah, but the real reason is their uni can't afford a PET scanner for them to use.

"The gold standard for CBF and CMRO2 measurements is 15O PET; but this technique requires an on-site cyclotron, a sophisticated imaging setup and substantial experience in handling three different radiotracers (CBF, 15O-water; CBV, 15O-CO; OEF, 15O-gas) of short half-lives8,35. Furthermore, this invasive method poses certain risks to participants owing to the exposure to radioactivity and arterial sampling."

Can the OP change the HN item title so scrollers don't think there is a problem with MRI? Isn't fMRI being questioned?

this headline is a bit misleading on the first read, since it only affects functional (f)MRI, which is controversial since a longer time. a prominent example is the activity that has been detected in a dead salmon

I wonder how much variation there is between a person who does certain mental activity regularly vs a person who rarely does it.

If they were to measure a person who performs mental arithmetic on a daily basis, I'd expect his brain activity and oxygen consumption to be lower than those of a person who never does it. How much difference would that make?

As someone who used to work at the Cognitive Neurophysiology Lab in the Scripts Institute-- doing some work on functional brain image-- I can confirm this was not news even thirty years ago. I guess this is trying to make some point to lay people?

you're telling me the results of this paper were likely bs? --- https://www.sciencedirect.com/science/article/abs/pii/S10538...

It's so much worse than this.

For task fMRI, the test-retest reliability is so poor it should probably be considered useless or bordering on pseudoscience, except for in some very limited cases like activation of the visual and/or auditory and/or motor cortex with certain kinds of clear stimuli. For resting-state fMRI (rs-fMRI), the reliabilities are a bit better, but also still generally extremely poor [1-3].

There are also two IMO major and devastating theoretical concerns re fMRI that IMO make the whole thing border on nonsense. One is the assumed relation between the BOLD signal and "activation", and two is the extremely horrible temporal resolution of fMRI.

It is typically assumed that the BOLD response (increased oxygen uptake) (1) corresponds to greater metabolic activity, and (2) increased metabolic activity corresponds to "activation" of those tissues. This trades dubiously on the meaning of "activation", often assuming "activation = excitatory", when we know in fact much metabolic activity is inhibitory. fMRI cannot distinguish between these things.

There are other deeper issues, in that it is not even clear to what extent the BOLD signal is from neurons at all (could be glia), and it is possible the BOLD signal must be interpreted differently in different brain regions, and that the usual analyses looking for a "spike" in BOLD activity are basically nonsense, since BOLD activity isn't even related to this at all, but rather the local field potential, instead. All this is reviewed in [4].

Re: temporal resolution, essentially, if you pay attention to what is going on in your mind, you know that a LOT of thought can happen in just 0.5 seconds (think of when you have a flash of insight that unifies a bunch of ideas). Or think of how quickly processing must be happening in order for us to process a movie or animation sequence where there are up to e.g. 10 cuts / shots within a single second. There is also just biological evidence that neurons take only milliseconds to spike, and that a sequence of spikes (well under 100ms) can convey meaningful information.

However, the lowest temporal resolutions (repetition times) in fMRI are only around 0.7 seconds. IMO this means that the ONLY way to analyze fMRI that makes sense is to see it as an emergent phenomenon that may be correlated with certain kinds of long-term activity reflecting cyclical BOLD patterns / low-frequency patterns of the BOLD response. I.e. rs-fMRI is the only fMRI that has ever made much sense a priori. The solution to this is maybe to combine EEG (extremely high temporal resolution, clear use in monitoring realtime brain changes like meditative states and in biofeedback training) with fMRI, as in e.g. [5]. But, it may still well be just the case fMRI remains mostly useless.

[1] Elliott, M. L., Knodt, A. R., Ireland, D., Morris, M. L., Poulton, R., Ramrakha, S., Sison, M. L., Moffitt, T. E., Caspi, A., & Hariri, A. R. (2020). What Is the Test-Retest Reliability of Common Task-Functional MRI Measures? New Empirical Evidence and a Meta-Analysis. Psychological Science, 31(7), 792–806. https://doi.org/10.1177/0956797620916786

[2] Herting, M. M., Gautam, P., Chen, Z., Mezher, A., & Vetter, N. C. (2018). Test-retest reliability of longitudinal task-based fMRI: Implications for developmental studies. Developmental Cognitive Neuroscience, 33, 17–26. https://doi.org/10.1016/j.dcn.2017.07.001

[3] Termenon, M., Jaillard, A., Delon-Martin, C., & Achard, S. (2016). Reliability of graph analysis of resting state fMRI using test-retest dataset from the Human Connectome Project. NeuroImage, 142, 172–187. https://doi.org/10.1016/j.neuroimage.2016.05.062

[4] Ekstrom, A. (2010). How and when the fMRI BOLD signal relates to underlying neural activity: The danger in dissociation. Brain Research Reviews, 62(2), 233–244. https://doi.org/10.1016/j.brainresrev.2009.12.004, https://scholar.google.ca/scholar?cluster=642045057386053841...

[5] Ahmad, R. F., Malik, A. S., Kamel, N., Reza, F., & Abdullah, J. M. (2016). Simultaneous EEG-fMRI for working memory of the human brain. Australasian Physical & Engineering Sciences in Medicine, 39(2), 363–378. https://doi.org/10.1007/s13246-016-0438-x

Why did TUM let this misleading headline front the news release? Dont we have enough issues with Academia? The result just mean BOLD is an imperfect proxy.